Systems and methods for identifying optimized ablation targets for treating and preventing arrhythmias sustained by reentrant circuit

ABSTRACT

Methods and systems for identifying optimized ablation targets for treating and preventing arrhythmias sustained by reentrant circuits are described. The methods comprise receiving at least one mesh generated from one or more images of a patient’s heart, receiving activation data generated from one or more simulations of electrical-signal propagation over the at least one mesh, generating at least one flow graph based on the activation data and the at least one mesh, and applying a max-flow min-cut algorithm to the at least one flow graph to determine at least one of a number, one or more dimensions, and one or more locations of one or more ablation targets. Non-transitory computer-readable media storing a set of instructions for treating and preventing arrhythmias sustained by reentrant circuits are also described.

PRIORITY CLAIM

This application claims priority from U.S. Provisional Pat. ApplicationNo. 62/262,874 filed on Dec. 3, 2015, which is hereby incorporated byreference in its entirety in the present application.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods foridentifying optimized ablation targets for treating and preventingarrhythmias sustained by reentrant circuits.

BACKGROUND

Catheter ablation is a therapy for various cardiac arrhythmias. Cardiacarrhythmia is a condition in which an organism’s heart beats tooquickly, too slowly, or in an irregular (e.g., non-periodic) manner. Amore precise name for the condition in which the heart beats too quicklyis “tachycardia.” A diagnosis of tachycardia may be given for aheartrate above a certain value (e.g., 100 beats per minute, or bpm, inadults). Tachycardia that originates in the ventricular region of theheart may be referred to as “ventricular tachycardia,” or “VT.”

One variety of VT is named “reentrant VT.” Reentrant VT is characterizedby improper electrical-signal propagation through the heart-musclecells, or “cardiomyocytes.” This may occur when cardiomyocytes arestressed or irritated by, for example, ischemia (i.e., an inadequateblood supply caused by, for example, a myocardial infarction), tissuenecrosis, drug reactions, or electrolyte imbalances. Such irritation mayalter conduction patterns in the heart and/or the conduction speed andrefractory period of the affected cardiomyocytes, resulting in improperelectrical-signal propagation. The improper pathways the electricalsignal takes are “reentrant pathways” or “reentrant circuits.” Reentrantpathways may be created also when a scar develops on the heart tissue,such as following a myocardial infarction. Reentrant pathways are closedpathways (e.g., a loop or circuit).

When the electrical signal takes a reentrant pathway, the ventricles maycontract and cause a heartbeat at an improper time and rate. A secondpropagation of an electrical signal over heart tissue following a recentfirst propagation does not, however, necessarily trigger an extraheartbeat; no contraction of the ventricles is triggered if anelectrical signal propagates during a refractory period of thecardiomyocytes. A scar on the heart tissue, however, may create a largeobstacle for the electrical signal to propagate around. If the patharound the scar is long enough, the electrical signal may take longer totravel around the scar than the refractory period of the cardiomyocytes.This may result in the cardiomyocytes around the scar being constantlyactivated by the electrical signal travelling on a reentrant pathwayaround the scar, thus resulting in an arrhythmia. A scar on the hearttissue may also itself conduct electrical signals through microchannelstherein, potentially forming reentrant pathways.

Catheter ablation is the process of using a catheter to, among otherthings, burn or freeze cardiomyocytes that form a reentrant pathway,creating scar tissue that will not conduct or causing a scar to conductmuch less than before ablation. Severing the reentrant pathway mayprevent the electrical signal from propagating down the reentrantpathway and triggering a heartbeat at an improper time and rate.

Determining the location or locations for ablation on the heart tissuepresents various difficulties. In making this determination, one mayseek to minimize the amount of heart tissue burned or frozen whenattempting to treat the tachycardia. Each lesion intentionally createdby burning or freezing involves a risk of collateral injury to theheart, such as, for example, one or more steam pops, perforations, andtamponades. Instead or in addition, one may seek to minimize the numberof ablations in order to limit the duration of the ablation procedure.Longer ablation procedures are associated with increased risk of chamberperforation, thromboemboli, bleeding, and radiation overexposure.

Because reentrant pathways are three-dimensional (3D) and may assumecomplicated shapes, determining the location or locations of ablationsuch that their number and size is minimized presents difficulties. Onemethod is to insert a catheter into the heart and record the voltage atthe catheter’s tip at each position in space to build a static surfacerepresentation of the heart and the voltages thereon. The resultingimages can be difficult to understand because the electrical signalchanges constantly on the heart’s surface and such changes may not becaptured by the static surface representation. Additionally, there is noconsensus in the medical community on the proper method for pickingablation locations based on the static surface representations. Thismethod and other methods requiring catheter-based mapping are verytime-consuming and error-prone because the catheter must be placed on ornear every relevant spot on the heart. In addition, these methods do notignore pathways that are not reentrant (e.g., dead-end pathways).Another method for determining the location or locations of ablationscomprises stimulating cardiac tissue with a catheter tip and recordingan electrocardiograph (ECG). The recorded ECG may be compared to asecond ECG that captures the arrhythmic activity to attempt determiningwhether the catheter tip was on a reentrant pathway. Even if thiscomparison reveals a reentrant pathway, it does not indicate where toablate.

Other methods using software-run 3D simulations are being explored todetermine ablation targets. Using software-run 3D simulations mayovercome many of the deficiencies of the foregoing methods. Software-run3D simulations may overcome many of the deficiencies because they enableprocessing of large amounts of information in short periods of timewithout manual human intervention, leading to fewer missed spots anderrors in data collection inherent in the previously discussed methods.Using simulations, unlike other methods, may not require cathetermapping or heart stimulation. Currently, however, when presented withsimulation data describing the electrical-signal propagation on andthrough heart tissue over time in a 3D simulation, clinicians may havetrouble picking ablation locations such that the number and size ofablations is minimized. To pick an ablation location, electrical-signallevels may need to be repeatedly observed in the simulation at many orall locations in the ventricle simulation at many or all points in timefor which data is available. Picking an ablation location with thisprocess is further complicated by the fact that the electrical signalmay propagate not only along the inner and outer surface of the tissuebut also within or through the tissue. Picking an ablation location byobserving the simulation may also be difficult because the simulationshows electrical-signal propagation along pathways that eventually getblocked by, for example, a scar or cardiomyocytes in their refractoryperiod. Such blocked propagations may obscure actual reentrant pathwaysthe clinicians seek to find in the simulation. FIG. 1A and FIG. 1B areillustrations of exemplary 3D simulation views of electrical-signalpropagation on and through a heart 100 a and 100 b, respectively. Aclinician may attempt to observe views 100 a and 100 b to determineoptimal ablation locations. Catheter-ablation locations (i.e., ablationtargets) are optimal if they are at locations requiring the fewest andsmallest ablations to treat and prevent future arrhythmia. Views 100 aand 100 b show electrical-signal propagations that may eventually getblocked by, for example, scar tissue, and therefore highlight pathwaysthat are not reentrant pathways. Additionally, a clinician may need toobserve cross sections of views 100 a and 100 b to study the electricalsignal propagation through the heart tissue’s interior. Because of thequantity of information presented in views 100 a and 100 b, isolatingthe reentrant pathways and finding the optimal ablation locations may bevery time-consuming or impossible.

The requisite observations in a software-based simulation may be verytime-consuming and, if many locations are not observed at many points oftime, may lead to over-selection of locations for ablation thannecessary to treat the VT, or selection of locations that do not helptreat the VT. Even if a clinician observes the electrical-signalpropagation at all locations at all points of time, he or she may needto consider this enormous amount of information simultaneously todetermine the proper locations to ablate and the proper ablation size.Success in this process requires much training, intuition, and expertiseon the part of the clinician. This decreases the number of cliniciansavailable to perform the analysis and increases procedure costs. Evenwith requisite training and expertise, clinicians may make errors whenpicking ablation locations based on observations of a simulation. Theuse of software-based simulations may have the benefit of providingclinicians with more data and data that is more accurate, but it mayoverwhelm and obscure the information the clinician is looking for.

The disclosed systems and methods are directed to overcoming one or moreof the problems set forth above and/or other problems or shortcomings inthe prior art.

SUMMARY

The present disclosure is directed to systems and methods foridentifying optimized ablation targets for treating and preventingarrhythmias sustained by reentrant circuits.

Consistent with at least one disclosed embodiment, a method is disclosedfor identifying optimized ablation targets for treating and preventingarrhythmias sustained by reentrant circuits. The method may includereceiving at least one mesh generated from one or more images of apatient’s heart. The method may also include receiving activation datagenerated from one or more simulations of electrical-signal propagationover the at least one mesh. The method may further include generating atleast one flow graph based on the activation data and the at least onemesh. The method may also include applying a max-flow min-cut algorithmto the at least one flow graph to determine at least one of a number,one or more dimensions, and one or more locations of one or moreablation targets.

In certain embodiments, the at least one mesh may comprise one or moreelements. In exemplary embodiments, the activation data may comprise oneor more activation times associated with one or more parts of theelements. In an embodiment, the at least one flow graph may comprise oneor more nodes associated with the one or more elements.

Identifying optimized ablation targets may also include designating oneor more nodes as source nodes and designating one or more nodes as sinknodes. In certain embodiments, the at least one flow graph may compriseone or more edges connecting two or more nodes associated with one ormore elements. In an exemplary embodiment, the one or more edges maconnect two or more nodes associated with two or more elements that arejoined.

Identifying optimized ablation targets may also include calculating oneor more capacity associated with the one or more edges. Identifyingoptimized ablation targets may also include calculating one or morecapacity associated with the one or more edges; creating a combinationof nodes; and designating the combination of nodes as a source, whereinthe combination is created such that the combination comprises thesmallest number of nodes and the sum of the one or more capacitiesassociated with the one or more edges emanating from the nodes in thecombination is above a threshold.

Identifying optimized ablation targets may also include calculating oneor more capacity associated with the one or more edges; creating acombination of nodes; and designating the combination of nodes as asink, wherein the combination is created such that the combinationcomprises the smallest number of nodes and the sum of the one or morecapacities associated with the one or more edges terminating into thenodes in the combination is above a threshold.

Identifying optimized ablation targets may also include calculating oneor more capacity associated with the one or more edges; creating acombination of nodes; and designating the combination of nodes as asource, wherein the combination is created such that the combinationcomprises the smallest number of nodes and edges emanating from nodes inthe combination have non-zero residual capacities.

Identifying optimized ablation targets may also include calculating oneor more capacity associated with the one or more edges; creating acombination of nodes; and designating the combination of nodes as asink, wherein the combination is created such that the combinationcomprises the smallest number of nodes and edges terminating into nodesin the combination have non-zero residual capacities.

Identifying optimized ablation targets may also include determining oneor more directions for the one or more edges. Identifying optimizedablation targets may also include determining one or more residualcapacities for one or more edges. Identifying optimized ablation targetsmay also include eliminating one or more edges that do not form part ofa path from a source node to a sink node. Identifying optimized ablationtargets may also include displaying the at least one of a number, one ormore dimensions, and one or more locations of one or more ablationtargets.

Identifying optimized ablation targets may also include creating atleast two flow-graph segments out of at least one flow-graph by removingan edge from the flow graph, adding a source to one of at least twoflow-graph segments, and adding a sink to one of at least two flow-graphsegments. In certain embodiments, one or more nodes may be associatedwith elements that were activated within one or more time windows. In anexemplary embodiment, the one or more nodes may be associated withelements that were activated at one or more times.

Identification of optimized ablation targets may also includedetermining whether a reentrant pathway is present. In certainembodiments, the one or more capacities may be based on one or moremeasures of intersection of the two or more elements that are joined.

In another aspect, the present disclosure is directed to a system foridentifying optimized ablation targets for treating and preventingarrhythmias sustained by reentrant circuits. The system may include amemory device and a central processing unit (CPU). The CPU mayprogrammed with instructions to receive at least one mesh generated fromone or more images of a patient’s heart. The CPU may also be programmedwith instructions to receive activation data generated from one or moresimulations of electrical-signal propagation over the at least one meshand generate at least one flow graph based on the activation data andthe at least one mesh. The CPU may also be programmed with instructionsto apply a max-flow min-cut algorithm to the at least one flow graph todetermine at least one of a number, one or more dimensions, and one ormore locations of one or more ablation targets.

In another aspect, the present disclosure may be directed tonon-transitory computer-readable medium storing a set of instructionsthat are executable by one or more processors to cause the one or moreprocessors to perform a method for identifying optimized ablationtargets for treating and preventing arrhythmias sustained by reentrantcircuits. The method may include receiving at least one mesh generatedfrom one or more images of a patient’s heart. The method may furtherinclude receiving activation data generated from one or more simulationsof electrical-signal propagation over the at least one mesh. The methodmay also include generating at least one flow graph based on theactivation data and the at least one mesh. The method may furtherinclude applying a max-flow min-cut algorithm to the at least one flowgraph to determine at least one of a number, one or more dimensions, andone or more locations of one or more ablation targets.

Other embodiments of this disclosure are disclosed in the accompanyingdrawings, descriptions, and claims. Thus, this summary is exemplaryonly, and is not to be considered restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate the disclosed embodiments and,together with the description, serve to explain the principles of thevarious aspects of the disclosed embodiments. In the drawings:

FIG. 1A: Illustrates an exemplary 3D simulation view ofelectrical-signal propagation on and through a heart.

FIG. 1B: Illustrates an exemplary 3D simulation view ofelectrical-signal propagation on and through a heart.

FIG. 2 : Illustrates an exemplary reentrant circuit and optimizedablation target display.

FIG. 3 : Illustrates an exemplary MRI.

FIG. 4 : Illustrates an exemplary polyhedral mesh.

FIG. 5 : Illustrates an exemplary flow graph.

FIG. 6 : Illustrates an exemplary process for identifying optimizedablation targets for treating and preventing arrhythmias sustained byreentrant circuits.

FIG. 7 : Illustrates an exemplary flow graph.

FIG. 8 : Illustrates an exemplary process for identifying optimizedablation targets for treating and preventing arrhythmias sustained byreentrant circuits.

FIG. 9 : Illustrates an exemplary flow graph.

FIG. 10 : Illustrates an exemplary system for identifying optimizedablation targets for treating and preventing arrhythmias sustained byreentrant circuits.

It is to be understood that both the foregoing general descriptions andthe following detailed descriptions are exemplary and explanatory onlyand are not restrictive of the claims.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Reference will now be made to certain embodiments consistent with thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to same or like parts.

The present disclosure describes systems and methods for identifyingoptimized ablation targets for treating and preventing arrhythmiassustained by reentrant circuits. Such systems and methods may generate aflow graph based on simulation data run over a polyhedral mesh in 3Dspace, which may be a volumetric mesh, of a patient’s heart. The meshmay comprise polyhedrons of any variety, including, but not limited to,tetrahedrons and/or hexahedrons. In certain embodiments, the mesh may bea polygon mesh or a mesh of another variety. A max-flow min-cutalgorithm (MFMC algorithm), or any variation thereof, may be run overthe flow graph to determine the minimum number and minimum size ofablations necessary to terminate electrical-signal propagation overreentrant pathways. Such systems and methods may be used before and/orduring ablation treatment to identify one or more ablation targets. Themethods may be performed once before or during treatment or may beperformed multiple times before and/or throughout a treatment procedure.

Software-run 3D simulations of electrical-signal propagation acrossheart tissue may be useful in identifying, treating, and preventingarrhythmias sustained by reentrant circuits if the simulations minimizenon-pertinent data and calculate optimized catheter-ablation targets.FIG. 2 is an illustration of an exemplary reentrant circuit andoptimized ablation target display 200. Display 200 may comprise one ormore 3D models of a heart or heart tissue 210, one or more reentrantpathway 220, and one or more optimal catheter-ablation target 230.Catheter-ablation targets (i.e., ablation targets) are optimal if theyare at locations requiring the fewest and smallest ablations to treatand prevent future arrhythmia.

The identification of an optimal ablation target or targets may, inillustrative embodiments, be performed by a system capable of generatinga flow graph from a simulation of electrical-signal propagation over apatient’s heart tissue. The simulation may be run over a polyhedral meshconstructed from one or more cardiac imaging modalities-such as, forexample, Magnetic Resonance Images (MRIs), echocardiographic images,and/or computerized tomography scans-of the patient’s heart. FIG. 3 isan illustration of an exemplary MRI 300. The one or more MRIs 300 may beinput into processing software. This inputting may be performed by animage-scanning device, by sending MRls 300 over a network to a connectedcomputer running the processing software, or other conventionalimage-inputting means. Once input into the processing software, MRls 300may be segmented into various components by identifying exemplarysections 310 and 320 of the MRI to the processing software. Exemplarysection 310 may indicate ventricular tissue and exemplary section 320may indicate blood in an atrial cavity. Sections of the MRIs may beidentified as, for example, one or more of the following: healthytissue, a scar, a border zone between different parts of the heart, andblood in cavities. The segmentation may be performed by softwareconfigured to identify sections of the MRIs, by a user, or a combinationof both.

The one or more segmented MRls may be used to generate a polyhedral meshof the patient’s heart. FIG. 4 is an illustration of an exemplarypolyhedral mesh 400 of a section of a patient’s heart. In certainembodiments, the mesh may be a digitally generated mesh. The polyhedralmesh may be a collection of vertices (such as exemplary vertices 410 a,410 b, and 410 c) and edges (such as exemplary edge 420), and may be astructured, an unstructured, or both a structured and an unstructuredgrid. Vertices 410 a, 410 b, and 410 c may be points in 3D space. Edge420 may be a connection between two vertices such as vertices 410 a and410 b. The set of closed edges may define polyhedrons, such as exemplarypolyhedron 430. Polyhedron 430 may be the building block, or “element,”of polyhedral mesh (i.e., mesh) 400. In certain embodiments, if the meshwere a polygon mesh, a polygon would be the building block. In the caseof a polyhedron mesh, the polyhedrons may be, but are not limited to,for example, tetrahedrons, hexahedrons, or a mixture of different typesof polyhedrons. Polyhedral mesh 400 may be a conformal mesh. In certainembodiments, polyhedral mesh 400 may be a non-conformal mesh. Becauseadjacent polyhedral elements may be joined by a planar face with anappropriate number of sides in structured and unstructured meshes,various methods for calculating the area of a two-dimensional polygonmay be used to calculate the cross-sectional area of the interfacebetween two three-dimensional polyhedral elements. For examples, for atriangle, this could be one-half multiplied by the length of the basemultiplied by the height. Alternatively, the area of a triangle may becomputed as one-half the magnitude of the cross-product of twointersecting sides of the triangle. Similarly, the area of a rectangularinterface between two elements may be computed as the magnitude of thecross-product of two intersecting edges of the rectangle. In the case ofa polygon mesh, adjacent polygons may be joined by an edge, the lengthof which may be calculated.

In the illustrative simulation software, one or more virtual electrodesmay be placed on the mesh. A user may specify in the software thevirtual electrical stimulation to the heart mesh model when thesimulation is run. The stimulation may be specified for the one or moreelectrodes. The stimulation may be specified in such a manner thatelectrical-signal propagation occurs in the simulation consistent witharrhythmia. As the region of interest may be around a scar and/or aborder zone tissue, stimulations may be located near the periphery ofthe scar, such as at several locations evenly spaced around the boundaryof the scar. Other typical locations for stimulation may be theright-ventricular outflow tract and right-ventricular apex, as these maybe convenient locations for catheter-based stimulation of the heart in aclinical environment. Stimulation strength may be a multiple of theminimum current required to activate simulated tissue at rest, modeledby direct current injection to the cellular ionic models used in themesh (e.g., two or three times the minimum current). For example, theelectrodes may stimulate the heart mesh model in the simulation soelectrical-signal propagation consistent with ventricular tachycardia,or other relevant arrhythmia, is present in the simulation. Simulationsthat continue to exhibit activity (e.g., electrical-signal propagation)after termination of externally-applied pacing may be consideredsuccessful initiations of reentrant arrhythmia without furtherobservation of the simulation data. In certain embodiments, fullsimulation data may be visualized and analyzed in detail to verifyinitiation of reentrant arrhythmia.

Running the illustrative simulation may generate activation data. Thisdata may comprise a list of times the simulation detected a voltageabove a certain threshold at one or more vertices in the mesh (i.e.,activation times). This threshold may be ionic-model dependent and mayindicate an “activation” spike above the transmembrane voltage observedin a cardiac cell at rest (e.g., greater than -10 mV). In someembodiments, some function of the voltage threshold and a rate of changeof the transmembrane voltage (e.g., dV/dt) may be employed to eliminatefalse detections of activations that may stem from high current driveninto a refractory cell. The activation data may comprise the list ofvertices associated with the times the voltage was detected to be abovethe threshold at the respective vertices. The vertices and/or theiractivation times may be sorted from the earliest activated to the latestactivated.

Using the activation data and mesh, the system, according to anexemplary embodiment, may generate a flow graph for identifying ablationtargets, such as exemplary flow graph 500 illustrated in FIG. 5 . Thismay be accomplished using, for example, exemplary process 600illustrated in FIG. 6 . In certain embodiments the activation data maybe provided directly by the patient or other source independent of asimulation. The flow graph 500 (also referred to as a flow network) maybe a directed graph, made up of nodes (e.g., points), such as exemplarynodes 510 and 520, and edges, such as exemplary edges 530 and 540,connecting two or more nodes. In certain embodiments, flow graph 500 maybe an undirected graph. Nodes may represent structures, such aspolyhedron 430 of polyhedral mesh 400 in FIG. 4 . A pair of nodes may beconnected by an edge if there is capacity for movement of somethingbetween the structures represented by the pair of nodes. The capacityfor movement, as referenced herein, may, in some embodiments, refer to acapability of electrical-signal to propagate. The edges may have one ormore directions associated with them, indicating one or more directionsof the capacity for movement or the net capacity for movement betweenthe structures associated with the nodes connected by the edges. Suchone or more directions may be visualized as one or more arrowheads, suchas exemplary arrowhead 550, pointing toward the one or more directionsof the capacity for movement. One or more edges may have one or morecapacities associated with them, such as exemplary flow capacity 560,indicating the largest possible flow between the structures associatedwith the nodes connected by the edges as determined by, for example, thecross-sectional area of the common face between the structures. In thecase of a polygon mesh or a mesh comprising elements of other varieties,the length of the common edge between the polygons or other commonstructure may determine the capacity in the corresponding flow-graphedge. One or more edges may have a maximum flow amount (i.e., maximumflow) associated with them, describing how much net movement would occurbetween structures associated with the nodes connected by the edges ifthere was a maximum net movement between the source and the sink. Theflow graph may contain a source, such as exemplary source 570, and/or asink, such as exemplary sink 580, representing nodes that only havemovement coming out of them or only have movement coming into them,respectively, and their associated structures.

The exemplary system may generate flow-graph nodes for each element inthe mesh (e.g., for each tetrahedron) at step 610 of exemplary process600, illustrated in FIG. 6 . In some embodiments, the system maygenerate a node only for the elements that were activated. An elementmay be considered activated if, for example, one or more verticescomprising the element were activated. Other methods of determiningwhether an element was activated may be used. In some embodiments, thesystem may generate nodes only for elements that were activated at atime that is a multiple of a fixed period (e.g., elements that wereactivated every 30 milliseconds from the start of the simulation, suchas at 30 milliseconds, 60 milliseconds, 90 milliseconds, etc.). Anelement may be determined to be activated at a particular time using afunction of the times at which one or more vertices in the element wereactivated, such as the average time the element’s vertices wereactivated. The fixed period may be set higher for lower resolution(i.e., fewer nodes) and faster processing or set lower for higherresolution (i.e., more nodes) but slower processing. In someembodiments, the system may generate one or more nodes for one or moreelements that were activated within a window of time. In someembodiments, a single node may be generated for multiple elements. Insuch case, the elements associated with the node may be those that wereactivated at a certain time or within one or more windows of time. Thismay be referred to as “binning.” For example, a node may be generatedfor all elements activated within every 10 millisecond window from thebeginning to the end of the simulation. Such method may allow for outputablation targets to be of sufficient size and proximity to one anotherto prevent arrhythmia. The size of the window may be fixed or varied. Insome embodiments, a node may be generated for all elements activatedwithin one or more windows of time and that are within a fixed orvariable distance from one another.

The system, according to an illustrative embodiment, may generate edgesconnecting node-pairs at step 620. The edges may be generated if theelements represented by the node-pairs share a common face (e.g., twotetrahedrons having one side in common) and if the difference inactivation time of the two elements was below a threshold time (e.g., 30milliseconds). In certain embodiments, the difference in activation timemay itself determine that two nodes are connected. In certainembodiments, the sharing of a common face by elements represented by thenode-pairs is sufficient to connect the two nodes. Whether two elementshave a common face may be determined by examining the mesh. Thisexamination may comprise determining whether two elements share a numberof vertices equal to the number of sides the elements’ faces have. Theactivation time for a single element may be determined as a function ofthe activation times of the vertices the element consists of (e.g., theaverage of the activation times at the vertices of a tetrahedron). Theactivation times for individual vertices may be looked up in theactivation data. The difference in activation time between two elementsmay be determined by subtracting the activation time of one element fromthe activation time of the other. Other methods of determining whetheran element was activated and the time it was activated may be used. Inan exemplary embodiment, an edge may be associated with a plurality ofcommon faces. This may occur if, for example, these common faces areparts of a plurality of elements that are associated with a single node.In certain embodiments, this may occur when binning is performed. Incertain embodiments, other methods for connecting node-pairsrepresenting adjacent elements may be used.

The exemplary system may determine capacities for edges connecting twonodes at step 630. The edge capacities may be determined using, forexample, a function of the cross-sectional area of the common facebetween the two elements represented by the two nodes (e.g., roundingthe cross-sectional area to the nearest integer). In certainembodiments, the capacity for an edge connecting two nodes may bedetermined by dividing the area of the common face associated with theedge by a number (e.g., a small number) to produce an unsigned integer.Doing so may facilitate computing the maximum flow, as discussed below,by making the problem of calculating the maximum flow tractable. Themethod for calculating the edge capacity and the units used to representedge capacity may be irrelevant as long as they are applied consistentlyfor all edges. In certain embodiments, an edge that is associated with aplurality of common faces may have an edge capacity that is a functionof the cross-sectional areas of the common faces it is associated with,such as the sum of the cross-sectional areas of the common faces. Themay occur when, for example, binning is performed.

In certain embodiments, the system may determine one or more directionsof the edges at step 640. A direction for a given edge may indicate, forexample, which of the two elements represented by the connected nodeshad an activation after the other (this may be visualized as, forexample, an arrow from the first-activated node to the second-activatednode).

The illustrative system may determine which nodes are sources and whichnodes are sinks at step 650. The nodes identified as sources may bethose representing elements comprising one or more activated vertices atthe time the simulation began, or nodes that were generated after anedge was removed and designated as sources, as discussed in aforthcoming section. The nodes identified as sinks may be thoserepresenting elements comprising one or more activated vertices at thetime the simulation ended, or nodes that were generated after an edgewas removed and designated as sinks, as discussed in a forthcomingsection. In some embodiments, a combination of nodes may be combined toform a single source or sink. For example, if running a max-flow min-cutalgorithm over the flow graph, as discussed below, indicates that edgesemanating from the source should be removed, one or more nodes connectedto the source may be merged with the source to form a larger source.Similarly, if running a max-flow min-cut algorithm over the flow graph,as discussed below, indicates that edges terminating into a sink shouldbe removed, one or more nodes connected to the sink may be merged withthe sink to form a larger sink. In certain embodiments, the edgesconnecting combined nodes may be disregarded. In certain embodiments,sources may be combined with nodes to form larger sources until thecommon faces associated with the edges emanating from the larger sourcehave a total cross-sectional area that is larger than a threshold. Incertain embodiments, this threshold may be the minimum area of tissue aclinician determines may be ablated or that is practical to ablate.

The system, according to an embodiment of the disclosure, may analyzethe flow graph to remove dead-end paths in the graph at step 660 andgenerate a flow graph without dead-end paths, such as exemplary flowgraph 700 illustrated in FIG. 7 . A dead-end path, such as exemplarydead-end paths identified by dotted squares 590 a, 590 b, and 590 c ofexemplary flow graph 500, may be a segment of the flow graph that, whenone follows the edge directions, if the edges have directions, from anode representing an earlier-activated element to a node representing alater-activated element, the last node reached has no edges leaving thenode and is not a sink. In certain embodiments, connectivity analysismay be performed to determine which pathways lead to dead ends. Such adead-end path may indicate, for example, that a simulated electricalsignal flowed from one part of the tissue to another but was blocked byone or more of a scar, other structure, or refractory tissue before theend of the simulation. Ablating a section of the tissue represented bysuch a dead-end path may not terminate a reentrant pathway because areentrant pathway is not a dead-end pathway, but rather a continuousloop or circuit.

The illustrative system may determine which edges to remove fromflow-graph segments connecting the source to the sink (i.e., segmentsthat do not necessarily lead to a dead end) in order to optimally reduceflow through the flow graph to zero at step 670. A flow-graph segmentconnecting a source to a sink may be a collection of edges between asource and the first-encountered sink in the direction of the edges, ifthe edges have directions. Determining which edges to remove may beaccomplished using a known MFMC algorithm, such as the Boykov-Kolmogorovalgorithm. This algorithm may calculate the maximum flow through thegraph, which, per the Max-flow Min-cut theorem, will also be the totaledge capacity of the smallest cut (i.e., the removal of edges with thesmallest sum total of their capacities) that can be made in the graph toreduce flow to zero (i.e., the min-cut). A flow is reduced to zero when,after edges are removed, no net movement may occur from the source tothe sink in the flow graph. Calculating the maximum flow may compriseassigning a flow value to each edge such that net movement between thesource and the sink, were movement to occur, is maximized. In certainembodiments, residual capacities may be calculated for each edge. Theresidual capacity calculated for an edge may be the difference betweenthe edge’s capacity and the edge’s calculated maximum flow. In certainembodiments, the edges to be removed (i.e., cut) may be those with aresidual capacity equal to zero. These removed edges may correspond tothe common faces of elements on which optimal ablations may beindicated, informing the user where in the heart to ablate to preventthe observed reentrant activation. This process may indicate thesmallest cross-sectional area necessary to ablate to prevent theobserved reentrant activation. In exemplary flow graph 700, for example,edges 710 and 720 may be identified if their residual capacities arezero under maximum flow conditions and they make up segments between asource and a sink. This may be repeated for each flow-graph segmentconnecting a source to a sink, thereby indicating edges with residualcapacities of zero for each such segment.

In certain embodiments, the common faces associated with the identifiededges may be highlighted to the user on the mesh as candidates foroptimal ablation at step 680. In some embodiments, edges with a capacityabove a threshold capacity are not highlighted for the user. Thisthreshold capacity may be set empirically and/or depending upon userpreference, such as if the user determines an area above which areas maynot be effectively covered with ablation-created lesions (e.g., morethan twenty square millimeters). In some embodiments, all common facesassociated with edges making up the pathways from the source to the sinkmay be highlighted instead or in addition to the common faces associatedwith the identified edges. If all common faces associated with edgesmaking up the pathways from the source to the sink are highlighted inaddition to the common faces associated with the identified edges, incertain embodiments, the common faces associated with the identifiededges may be highlighted in a different manner (e.g., in a differentcolor). In certain embodiments, arrows showing the connection betweenhighlighted common faces may be displayed to the user to show the edgeconnections between the nodes corresponding to the highlighted faces,thereby indicating the electric-signal. In some embodiments, segmentsconnecting the source and the sink that comprise a number of edges thatis below a threshold number of edges are disregarded. For example, if itis considered that no reentrant activation may have a cycle length ofless than 100 ms in a human heart, then flow-graph segments whosesources and sinks are connected by activation times of a duration lessthan 100 ms may be disregarded.

In certain embodiments, after a set of edges associated with a locationfor ablation is identified in each segment, the segments may be severedby removing those edges and new, shorter segments may be created atsteps 890 a, 890 b, and 890 c of exemplary process 800 illustrated inFIG. 8 . This may result in a flow graph similar to flow graph 900illustrated in FIG. 9 . Steps 810 through 880 of process 800 may besimilar to steps 610 through 680 of process 600. Exemplary process 800may comprise determining whether additional ablation points are to beidentified at step 890 a. At step 890 b, if additional ablation pointsare to be identified, edges identified in step 870 are removed. Theseidentified edges may be edges 710 and 720 in exemplary flow graph 700.Next, the nodes from which the removed edges emanated, such as nodes 910a and 910 b, may be defined as sinks. The nodes to which the removededges pointed, such as nodes 940 a and 940 b, may be defined as sources.Thus, the original sources may be connected to a new sink at the cutboundary by a flow graph and the original sink may be connected to a newsource on the other side of the cut boundary. The foregoing method forfinding the maximum flow through the edges and finding the edges with aresidual capacity of zero and highlighting the associated common facesmay be repeated for the newly created segments connecting sources andsinks. The process of creating smaller segments and finding andhighlighting more locations for ablation may be repeated a set number oftimes or until the max flow in each newly-created segment is larger thanthe capacity threshold above which edges are removed. This may be doneto decrease the chances of a terminated reentrant pathway beingactivated by a conduction pathway that was not identified by thesimulation.

In certain embodiments, the foregoing methods for identifying optimizedablation targets may be performed after determining whether a reentrantpathway is detected. In an exemplary embodiment, the method may beperformed based on whether a reentrant pathway is detected. A reentrantpathway may be detected by, for example, observing a point on hearttissue model under simulated stimulation for multiple activations aftera fixed or variable period of time. Such period of time may be based on,for example, clinical data indicating how much time must pass afterelectrode stimulation for no further activations to occur. In certainembodiments, the presence of activation after this period of time mayindicate the presence of a reentrant pathway.

A system for identifying optimized ablation targets for treating andpreventing arrhythmias sustained by reentrant circuits is illustrated inFIG. 10 as exemplary system 1000. The various components of system 1000may include an assembly of hardware, software, and/or firmware,including a memory device 1100, a central processing unit (“CPU”) 1200,and/or an optional user interface unit (“I/O Unit”) 1400. Memory device1100 may include any type of RAM or ROM embodied in a physical storagemedium, such as magnetic storage including floppy disk, hard disk, ormagnetic tape; semiconductor storage such as solid state disk (SSD) orflash memory; optical disc storage; or magneto-optical disc storage. TheCPU 1200 may include one or more processors, such as processor 1500, forprocessing data according to a set of programmable instructions 1300 orsoftware stored in the memory device 1100. The functions of eachprocessor 1500 may be provided by a single dedicated processor 1500 orby a plurality of such processors. Moreover, the one or more processors1500 may include, without limitation, digital signal processor (DSP)hardware, or any other hardware capable of executing software. Anoptional user interface (“I/O Unit”) 1400 may comprise any type orcombination of input/output devices, such as a display monitor,keyboard, touch screen, and/or mouse. The I/O Unit 1400 may receive mesh1600 and activation data 1700. The processor 1500 may executeinstructions 1300 causing the system to output ablation target data 1800through the I/O Unit 1400.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. For example,some embodiments discussed above pertain to ventricular tachycardia. Thesystems and methods described herein may also be used to treat othertypes of arrhythmia, such as, for example, atrial flutter. As anotherexample, the systems and methods for converting electrical-signalpropagation data into a flow graph may be applied in other fields, suchas converting the propagation of any time-varying field or energy alonga physical structure into a flow graph. A MFMC algorithm may be run onsuch flow graph to determine an optimal way to alter the propagationalong the physical structure.

The features and advantages of the disclosure are apparent from thedetailed specification, and thus, it is intended that the appendedclaims cover all systems and methods falling within the true spirit andscope of the disclosure. As used herein, the indefinite articles “a” and“an” mean “one or more.” Similarly, the use of a plural term does notnecessarily denote a plurality unless it is unambiguous in the givencontext. Words such as “and” or “or” mean “and/or” unless specificallydirected otherwise. Further, since numerous modifications and variationswill readily occur from studying the present disclosure, it is notdesired to limit the disclosure to the exact construction and operationillustrated and described, and accordingly, all suitable modificationsand equivalents may be resorted to, falling within the scope of thedisclosure.

Computer programs, program modules, and code based on the writtendescription of this specification, such as those used by themicrocontrollers, are readily within the purview of a softwaredeveloper. The computer programs, program modules, or code can becreated using a variety of programming techniques. For example, they canbe designed in or by means of Java, C, C++, assembly language, or anysuch programming languages. One or more of such programs, modules, orcode can be integrated into a device system or existing communicationssoftware. The programs, modules, or code can also be implemented orreplicated as firmware or circuit logic.

Another aspect of the disclosure is directed to a non-transitorycomputer-readable medium storing instructions which, when executed,cause one or more processors to perform the methods of the disclosure.The computer-readable medium may include volatile or non-volatile,magnetic, semiconductor, tape, optical, removable, non-removable, orother types of computer-readable medium or computer-readable storagedevices. For example, the computer-readable medium may be the storageunit or the memory module having the computer instructions storedthereon, as disclosed. In some embodiments, the computer-readable mediummay be a disc or a flash drive having the computer instructions storedthereon.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments include equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1. A method for identifying optimized ablation targets for treating andpreventing arrhythmias sustained by reentrant circuits, the methodcomprising: receiving at least one mesh generated from one or moreimages of a patient’s heart; receiving activation data generated fromone or more simulations of electrical-signal propagation over the atleast one mesh; generating at least one flow graph based on theactivation data and the at least one mesh; applying a max-flow min-cutalgorithm to the at least one flow graph to determine at least one of anumber, one or more dimensions, and one or more locations of one or moreablation targets. 2-23. (canceled)